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Course Outline
1. Grasping Classification via Nearest Neighbors
- The k-Nearest Neighbors (kNN) algorithm
- Distance calculation
- Selecting an optimal k value
- Data preparation for kNN application
- Understanding the lazy nature of the kNN algorithm
2. Comprehending Naive Bayes
- Core principles of Bayesian methods
- Probability theory
- Joint probability
- Conditional probability and Bayes' theorem
- The Naive Bayes algorithm
- Naive Bayes classification techniques
- The Laplace estimator
- Handling numeric features with Naive Bayes
3. Understanding Decision Trees
- Divide and conquer strategies
- The C5.0 decision tree algorithm
- Selecting optimal splits
- Pruning decision trees
4. Exploring Classification Rules
- Separate and conquer approaches
- The One Rule algorithm
- The RIPPER algorithm
- Deriving rules from decision trees
5. Deep Dive into Regression
- Simple linear regression
- Ordinary least squares estimation
- Correlations
- Multiple linear regression
6. Mastering Regression and Model Trees
- Integrating regression into trees
7. Insights into Neural Networks
- From biological neurons to artificial neurons
- Activation functions
- Network topology
- Layer configuration
- Information flow direction
- Node distribution across layers
- Training neural networks using backpropagation
8. Decoding Support Vector Machines
- Classification using hyperplanes
- Maximizing the margin
- Handling linearly separable data
- Addressing non-linearly separable data
- Applying kernels for non-linear spaces
9. Unpacking Association Rules
- The Apriori algorithm for learning association rules
- Evaluating rule relevance through support and confidence
- Constructing rule sets using the Apriori principle
10. Grasping Clustering
- Clustering as a machine learning task
- The k-means clustering algorithm
- Utilizing distance for cluster assignment and updates
- Determining the appropriate number of clusters
11. Assessing Classification Performance
- Managing classification prediction data
- An in-depth look at confusion matrices
- Utilizing confusion matrices for performance measurement
- Beyond accuracy: alternative performance metrics
- The kappa statistic
- Sensitivity and specificity
- Precision and recall
- The F-measure
- Visualizing performance trade-offs
- ROC curves
- Forecasting future performance
- The holdout method
- Cross-validation
- Bootstrap sampling
12. Optimizing Models for Enhanced Performance
- Leveraging caret for automated parameter tuning
- Developing a basic tuned model
- Customizing the tuning workflow
- Boosting model performance through meta-learning
- Understanding ensemble methods
- Bagging
- Boosting
- Random forests
- Training random forests
- Evaluating random forest performance
13. Deep Learning Overview
- Three primary categories of Deep Learning
- Deep Autoencoders
- Pre-trained Deep Neural Networks
- Deep Stacking Networks
14. Discussion of Specific Application Areas
21 Hours
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Very flexible.
